SPL (Search Processing Language) is the foundation of working with Splunk, and it is one of the first areas interviewers focus on for Splunk, SIEM, and SOC roles. Even for experienced candidates, interviewers often start with SPL basics to check how well you understand searching, filtering, and analyzing data before moving to advanced topics like dashboards, correlation searches, or performance tuning.
This blog is written specifically for interview preparation. It explains SPL basics using an interview-style question-and-answer format, with clear explanations, examples where needed, and practical pointers. The focus is on SPL syntax, common search commands, data filtering, and how SPL fits into Splunk’s overall search pipeline.
Interview Questions and Answers on SPL Basics
Question 1: What is SPL in Splunk?
Answer: SPL stands for Search Processing Language. It is the language used in Splunk to search, filter, analyze, and visualize machine data. SPL allows users to retrieve events from indexes, apply transformations, extract fields, and generate meaningful insights.
In simple terms, SPL tells Splunk what data to look for and how to process it. In interviews, it is important to mention that SPL works on indexed data and is executed mainly across indexers with coordination from the Search Head.
Question 2: What is the basic structure of an SPL query?
Answer: An SPL query generally follows a pipeline structure, where the output of one command becomes the input of the next command. Commands are separated using the pipe symbol (|).
A basic SPL structure includes:
- A search command to retrieve data
- One or more transforming or filtering commands
For example, a search may first retrieve events and then filter or aggregate them. In interviews, you can explain that SPL reads from left to right, and order matters.
Question 3: What is the difference between search and where commands?
Answer: The search command is used to retrieve and filter events early in the search pipeline. It is fast and optimized because it can leverage indexed fields.
The where command is used later in the pipeline and works on search results, not raw indexed data.
For example, filtering by index or sourcetype should be done using search, while filtering based on calculated fields often uses where. Interviewers expect you to understand this performance-related difference.
Question 4: How do you filter data in SPL?
Answer: Data filtering in SPL can be done using several methods, depending on the requirement.
Common filtering techniques include:
- Using search keywords
- Filtering by indexed fields such as index, sourcetype, host
- Using comparison operators
- Applying where conditions
For example, filtering early in the search reduces the amount of data processed later, which improves performance. In interviews, emphasize the importance of early filtering.
Question 5: What are some commonly used SPL commands?
Answer: Some of the most commonly used SPL commands include:
- search – retrieves events
- stats – performs aggregation like count or sum
- table – displays selected fields in tabular format
- sort – sorts results
- eval – creates calculated fields
- timechart – creates time-based visualizations
Question 6: What does the stats command do?
Answer: The stats command is used to perform statistical calculations on search results. It can calculate counts, averages, sums, minimums, and maximums.
For example, stats can be used to count the number of events per host or calculate the average response time. In interviews, mention that stats transforms raw events into aggregated results, which is useful for reporting and dashboards.
Question 7: What is the eval command used for?
Answer: The eval command is used to create new fields or modify existing fields using expressions. It supports mathematical operations, string manipulation, and conditional logic.
For example, eval can be used to classify events based on severity or calculate response times. Interviewers often check whether you know that eval operates at search time and does not change indexed data.
Question 8: How does SPL handle time in searches?
Answer: Time is a core concept in SPL. Searches are usually constrained by a time range, and each event has a time field that represents when the event occurred.
Time-based commands like timechart use the _time field to group data into intervals. In interviews, emphasize that correct timestamp extraction at index time is critical for accurate SPL results.
Question 9: What is the difference between stats and eventstats?
Answer: stats aggregates data and returns only the aggregated results, removing the original events.
eventstats calculates statistics but adds the results back to each event, preserving the original events.
For example, if you want to calculate an average and still keep individual events, eventstats is preferred. Interviewers often ask this to test deeper understanding of SPL behavior.
Question 10: How do you remove duplicate events in SPL?
Answer: Duplicate events can be removed using the dedup command. It keeps only the first occurrence of an event based on specified fields.
For example, dedup user can remove duplicate events for the same user. In interviews, you can mention that dedup is often used during investigations to reduce noise.
Question 11: What is the importance of using indexed fields in SPL?
Answer: Indexed fields such as index, sourcetype, source, and host make searches faster because Splunk can filter data before scanning raw events.
Using indexed fields early in the search pipeline reduces workload on indexers and improves performance. Interviewers often expect you to connect SPL usage with performance optimization.
Question 12: How does SPL work in a distributed environment?
Answer: In a distributed environment, the Search Head parses the SPL query and sends relevant parts to indexers. Indexers execute the search on their local data and return results.
The Search Head then aggregates and formats the final output. In interviews, this demonstrates understanding of how SPL fits into Splunk architecture.
Question 13: What are transforming and non-transforming commands?
Answer: Non-transforming commands, such as search or eval, operate on events and pass events down the pipeline.
Transforming commands, such as stats or timechart, transform events into aggregated results.
Question 14: How do you optimize SPL queries?
Answer: SPL optimization focuses on reducing the amount of data processed and simplifying commands.
Best practices include:
- Filtering early using indexed fields
- Avoiding unnecessary wildcard searches
- Using stats instead of table where possible
- Limiting result sets
Question 15: How would you explain SPL basics to a beginner?
Answer: I explain SPL as a pipeline where you first find data, then refine it, and finally analyze it. Each command performs one step, and results flow from one command to the next.
This simple explanation helps beginners understand SPL before diving into advanced use cases. Interviewers appreciate candidates who can explain complex topics clearly.
Conclusion
SPL basics form the foundation of all Splunk use cases, from simple searches to advanced SIEM detections and dashboards. Interviewers look for candidates who not only know SPL syntax, but also understand how commands work together, how searches are optimized, and how SPL fits into Splunk architecture. Mastering SPL basics with clear explanations and practical examples significantly improves your confidence and performance in Splunk interviews.
